Abstract
Breast screening is an effective method to identify breast cancer in asymptomatic women; however, not all exams are read by radiologists specialized in breast imaging, and missed cancers are a reality. Deep learning provides a valuable tool to support this critical decision point. Algorithmically, accurate assessment of breast mammography requires both detection of abnormal findings (object detection) and a correct decision whether to recall a patient for additional imaging (image classification). In this paper, we present a multi-task learning approach, that we argue is ideally suited to this problem. We train a network for both object detection and image classification, based on state-of-the-art models, and demonstrate significant improvement in the recall vs no recall decision on a multi-site, multi-vendor data set, measured by concordance with biopsy proven malignancy. We also observe improved detection of microcalcifications, and detection of cancer cases that were missed by radiologists, demonstrating that this approach could provide meaningful support for radiologists in breast screening (especially non-specialists). Moreover, we argue that this multi-task framework is broadly applicable to a wide range of medical imaging problems that require a patient-level recommendation, based on specific imaging findings.
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Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E.: A region based convolutional network for tumor detection and classification in breast mammography. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 197–205. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_21
Akselrod-Ballin, A., et al.: Deep learning for automatic detection of abnormal findings in breast mammography. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 321–329. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_37
Fu, C.Y., Shvets, M., Berg, A.C.: RetinaMask: learning to predict masks improves state-of-the-art single-shot detection for free. arXiv preprint arXiv:1901.03353 (2019)
Gao, F., Yoon, H., Wu, T., Chu, X.: A feature transfer enabled multi-task deep learning model on medical imaging. Expert Syst. Appl. 143, 112957 (2020)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Jung, H., et al.: Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS ONE 13(9), e0203355 (2018)
Kim, H.E., et al.: Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. The Lancet Digit. Health 2(3), e138–e148 (2020)
Le, T.L.T., Thome, N., Bernard, S., Bismuth, V., Patoureaux, F.: Multitask classification and segmentation for cancer diagnosis in mammography. In: International Conference on Medical Imaging with Deep Learning - Extended Abstract Track, London, UK (2019)
Lehman, C.D., et al.: National performance benchmarks for modern screening digital mammography: update from the breast cancer surveillance consortium. Radiology 283(1), 49–58 (2017)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Lotter, W., Sorensen, G., Cox, D.: A multi-scale CNN and curriculum learning strategy for mammogram classification. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 169–177. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_20
Mainiero, M.B., Parikh, J.R.: Recognizing and overcoming burnout in breast imaging. J. Breast Imaging 1(1), 60–63 (2019)
McKinney, S.M., et al.: International evaluation of an AI system for breast cancer screening. Nature 577(7788), 89–94 (2020)
Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 1–7 (2018)
Seaman, S.R., White, I.R.: Review of inverse probability weighting for dealing with missing data. Stat. Methods Med. Res. 22(3), 278–295 (2013)
Tabár, L., et al.: The incidence of fatal breast cancer measures the increased effectiveness of therapy in women participating in mammography screening. Cancer 125(4), 515–523 (2019)
Teare, P., Fishman, M., Benzaquen, O., Toledano, E., Elnekave, E.: Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J. Digit. Imaging 30(4), 499–505 (2017). https://doi.org/10.1007/s10278-017-9993-2
International Agency for Research on Cancer: World Health Organization: Global cancer observatory database (2018)
Wu, N., et al.: Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging 39(4), 1184–1194 (2020)
Yala, A., Lehman, C., Schuster, T., Portnoi, T., Barzilay, R.: A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292(1), 60–66 (2019)
Yala, A., Schuster, T., Miles, R., Barzilay, R., Lehman, C.: A deep learning model to triage screening mammograms: a simulation study. Radiology 293(1), 38–46 (2019)
Zlocha, M., Dou, Q., Glocker, B.: Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 402–410. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_45
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Sainz de Cea, M.V., Diedrich, K., Bakalo, R., Ness, L., Richmond, D. (2020). Multi-task Learning for Detection and Classification of Cancer in Screening Mammography. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_24
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DOI: https://doi.org/10.1007/978-3-030-59725-2_24
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